Summary of Defan: Definitive Answer Dataset For Llms Hallucination Evaluation, by a B M Ashikur Rahman et al.
DefAn: Definitive Answer Dataset for LLMs Hallucination Evaluation
by A B M Ashikur Rahman, Saeed Anwar, Muhammad Usman, Ajmal Mian
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the issue of hallucinations in Large Language Models (LLMs), which have become ubiquitous in daily life applications. Despite their remarkable capabilities, LLMs often generate claims that contradict established facts, deviate from prompts, and produce inconsistent responses when presented with the same prompt multiple times. The authors introduce a comprehensive benchmark dataset comprising over 75,000 prompts across eight domains to measure hallucination in LLMs. The dataset is divided into two segments: one publicly available for testing and assessing LLM performance, and a hidden segment for benchmarking various LLMs. The authors tested six LLMs-GPT-3.5, LLama 2, LLama 3, Gemini, Mixtral, and Zephyr-and found that overall factual hallucination ranges from 59% to 82%. Prompt misalignment hallucination ranges from 6% to 95%, while average consistency ranges from 21% to 61%. The authors also found that LLM performance significantly deteriorates when asked for specific numeric information. This dataset demonstrates its efficacy and serves as a comprehensive benchmark for LLM performance evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Large Language Models (LLMs) sometimes get things wrong. These AI models are really good at understanding and generating human-like text, but they can also make mistakes like saying things that aren’t true or changing their answers when asked the same question again. The authors created a big test dataset with 75,000 prompts to see how well LLMs do in different areas. They found that most LLMs get around 60% of questions right, but some are much worse than others. They also saw that LLMs are really bad at answering specific numeric questions like math problems. This test dataset can help us figure out which AI models are the best and what they’re good or bad at. |
Keywords
» Artificial intelligence » Gemini » Gpt » Hallucination » Llama » Prompt
